The present invention relates to image segmentation, and more particularly, to segmentation of tubular structures in 3D images.
Computed tomography (CT) and magnetic resonance imaging (MRI) scans are frequently used to support diagnosis and analysis of cardiovascular and pulmonary anatomic structures. Such structures can appear as tubular structures in CT and MR images. Accordingly, segmentation, or extraction, of 3D tubular structures from 3D image data is a fundamental problem in medical imaging, and is an important component of clinical applications involving diagnosis (e.g., stenosis, aneurysm, etc.), surgical planning, anatomical modeling and simulation, and treatment verification.
Using manual segmentation techniques to segment 3D tubular structures typically requires too much tedious labor to be practical in clinical applications. Various fully automatic segmentation methods have been developed for segmenting vessels and other tubular structures. However, due to poor contrast, noise, and clutter that is common to medical images, it is often difficult for fully automatic segmentation methods to yield robust results. Furthermore, one may be interested in extracting only a subset, for example a specific path through a branching network of tubular structures. Therefore, there is a salient need for an interactive segmentation method that is mostly automatic, but accepts input from an operator to guide the segmentation in a particular direction, quickly correct for errant segmentations, and add branches to an existing segmentation result. Computational efficiency is crucial to such a semi-automatic segmentation method, so that the operator will not have to wait for segmentation results while interacting with data. Typical conventional automatic segmentation techniques have runtimes that are too slow for use in such an interactive segmentation method.